Demand Response Alert Service Based on Appliance Modeling
| Autor: | G.T. Andreou, Christos Diou, Kyriakos C. Chatzidimitriou, Ioanna-M. Chatzigeorgiou |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Control and Optimization Computer science 020209 energy Energy Engineering and Power Technology 02 engineering and technology 010501 environmental sciences appliance modeling 01 natural sciences Demand response 0202 electrical engineering electronic engineering information engineering artificial intelligence applied to power systems Electrical and Electronic Engineering data analytics Engineering (miscellaneous) 0105 earth and related environmental sciences Service (business) demand side management Renewable Energy Sustainability and the Environment End user Probabilistic logic flexibility demand response peak shaving smart grid Reliability engineering Smart grid Peaking power plant Electric power F1 score Energy (miscellaneous) |
| Zdroj: | Energies, Vol 14, Iss 2953, p 2953 (2021) Energies; Volume 14; Issue 10; Pages: 2953 |
| ISSN: | 1996-1073 |
| Popis: | Demand response has been widely developed during recent years to increase efficiency and decrease the cost in the electric power sector by shifting energy use, smoothening the load curve, and thus ensuring benefits for all participating parties. This paper introduces a Demand Response Alert Service (DRAS) that can optimize the interaction between the energy industry parties and end users by sending the minimum number of relatable alerts to satisfy the transformation of the load curve. The service creates appliance models for certain deferrable appliances based on past-usage measurements and prioritizes households according to the probability of the use of their appliances. Several variations of the appliance model are examined with respect to the probabilistic association of appliance usage on different days. The service is evaluated for a peak-shaving scenario when either one or more appliances per household are involved. The results demonstrate a significant improvement compared to a random selection of end users, thus promising increased participation and engagement. Indicatively, in terms of the Area Under the Curve (AUC) index, the proposed method achieves, in all the studied scenarios, an improvement ranging between 41.33% and 64.64% compared to the baseline scenario. In terms of the F1 score index, the respective improvement reaches up to 221.05%. |
| Databáze: | OpenAIRE |
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